Virtual patients could help flag medical device risks missed by conventional studies
University of Manchester–led researchers have unveiled a roadmap for making computer-generated evidence trustworthy enough to help regulators decide whether medical devices are safe and effective, alongside laboratory and clinical evidence.
A University of Manchester-led team has developed a framework aimed at making computer-generated evidence trustworthy enough to assist regulators in determining the safety and efficacy of medical devices. Published in the journal Device, the roadmap was created by researchers from academia, industry and the UK Medicines and Healthcare products Regulatory Agency.
The framework outlines how in silico methods can generate credible digital evidence for specific regulatory decisions and provides guidance on the credibility of these models. Medical devices are currently tested through laboratory experiments, animal studies and clinical trials, but these methods may not always predict how a device will function in humans, and clinical trials often underrepresent certain populations.
Computer simulations, based on real patient anatomy, could help address these gaps by testing how a device behaves in virtual patients. However, manufacturers still lack a clear route for determining which potential harms to model and how to ensure the credibility of these simulations. The new framework addresses this by filtering potential harms using three key questions: the relevance of the harm to the regulatory decision, the presence of a plausible causal pathway linking the design change to the harm, and the ability of simulations to provide evidence that complements laboratory and clinical data.
By answering these questions, manufacturers and regulators can assess the reliability of the model, its alignment with real-world measurements, and the certainty of its predictions, including tests of assumptions and natural variation. This risk-informed approach could help create credible digital evidence, complement traditional testing methods, and support regulators in different countries in accepting the same evidence.
The framework was developed through UK CEiRSI's In Silico Regulatory Airlock, where regulators, industry and academics collaborate on real regulatory challenges.
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